{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment #2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import log_loss  \n",
    "from sklearn.metrics import accuracy_score\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xp68xCCRp+GrXMrgGeAo4CrgrIvquu2qpJA0z7cJgjzavSZKGkXajiX7TZCGSpM7ZnrWJ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\nGcRrTl9TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189aaf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#y值\n",
    "sns.countplot(data.Outcome);\n",
    "pyplot.xlabel('Outcome');\n",
    "pyplot.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0jYiUAV8F/iZRFVCZ69DpLnKys1im6+GmHE9eNn/3iTUXh3P+zx+/QVNLwO6w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\nZRBN+koplUH+f5CKLWYn91klAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b20ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Glucose.values, bins=30, kde=True)\n",
    "plt.xlabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Va9WFAEerwlM3YzLTksj1pZDrSyHHl0JOZjJZ6cm43RrION2iDfcf7a3lnboA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b5d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Pregnancies.values, bins=30, kde=True)\n",
    "plt.xlabel('Pregnancies', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eIifLg75ch7OkLasqAeAtcyU6kZ8kIYgl6532QcbjCdavKJNlHhfYsqCZEBp7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be259d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.BloodPressure.values, bins=30, kde=True)\n",
    "plt.xlabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A6gZZR0RERlgk4b4JuA0gNH6+L6ztEDDbGJNrjEnGGZLZMsg6IiIywjyDXTod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\n26eAWR88xYMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b15150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.SkinThickness.values, bins=30, kde=True)\n",
    "plt.xlabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R8VAYTMCBpuDA8PnDHDzOqa+ppCIepfWE9gxEZPZSGEzA/uZOYtEI5y6qHfE9kUiEBfUJ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bfd8b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Insulin.values, bins=30, kde=True)\n",
    "plt.xlabel('Insulins', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B+mS7aksDv+4aYXB8BrU588K7lueNXZ5rS7FrNqtz5VPca4HcCbNJpZRTa51Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c165e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.BMI.values, bins=30, kde=True)\n",
    "plt.xlabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8841rWbdUt3OT3AsGfLQ0VtLS6E6PSSSS9AyO0t4TSX0N89z+dp7b716IVlHm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3da190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.DiabetesPedigreeFunction.values, bins=30, kde=True)\n",
    "plt.xlabel('Diabetes Pedigree Function', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9nGcPOssef/vnt5MXWDbfvsi8Ys+u3bDSWYc/Oj5FV7ToO8OjnGjvp7Gtnx/v\nb8HjcS5yMlv2sWv3JX0si3brDI/wwE+OYU/1URsq4Is/d3ncu/aJLEe5OX5WVTorbAAmp2acou8d\noTM8QvOZAZo6Bnj81VY8QHlxLuVFASpLcwkV53LrVZruTDVXl3v/8AQ/2d/CMwdPMz0TYacJ8Rsf\n3qx/UoosUpbfS00on5qQs/hgcmqGrj5nVN/Z6+xx39U3Sn1LGIBn3jhNXVWhcwWr6kJqynVxk2Rb\nsOWMMV7gXmA7MA582lrbGHP8duAbwBSw11p7/0L3WSqRSISe/jHsqT5eP3aWIyd7mYlEKC8K8In3\nr2eXCenFJZIAWX5nR8zZXTFzc7NpOt1HZ9gp+sHhCd5o6OKNhq5z98nN8bOiLI/qsnwqS3Od/XOC\nOZQWBiguyCHLr/e/EimeIewdQMBau9sYczVwN/AxAGNMFnAPcCUwDOwzxjwCXDvffRLNtoZ5tf4s\nZ8MjtHcP0zc0ce7Ymuog115ezfXbVuiFI7KE/D4vVWV5VJXlAc5Aa2RsKrqNwhj9wxP0D01wsmOA\nE+0Dc36Ngtws8gJ+8gN+8gJZ5Af8ZGf58Pu8+H2ed//v9eL3e/F6PHg9znsGnuj/G9v6gOjHQCCQ\nxfj4VPQ4bFldCh7nXAGPx4MH537nf53Z23s8Hrz89DHvebc7/+O5vh44S7Cnp2eYnonQNzbF2a4h\nJianyfJ72biyOOEDz3jK/TrgcQBr7SvGmF0xxzYDjdbaMIAx5iVgD7D7AvdJqB/vbzn3rn5JMIed\nG0Osry3i8rVl50YVIpJcHo+H/Nws8nOzzs3bg1Nwm1eX0Nk7SnhwjN7BcXoHxgkPjjEwMsnw2CS9\nA+NMTc8sSa6XDp9Zkq97qe769fdRW1GQ0K8ZT7kXAv0xH08bY/zW2qk5jg0CRQvcZ06hUHBRv7ZC\nIecF82e/df1i7rbkZnMBfOLmTSlMIiLLWTxzFQNAMOZjb0xJn38sCPQtcB8REVli8ZT7PuA2gOj8\n+dsxx+qBDcaYUmNMNs6UzP4F7iMiIkvME4lELniDmJUv23Deo7gTuAIosNbeF7NaxouzWuav57qP\ntfbY0n0bIiISa8FyFxGRzKP1gSIiLqRyFxFxIZW7iIgLZcQmK8aYq4A/t9beaIxZDzwARIAjwBes\ntUtzxsOFM2UBe4E6IAf4E+CdVGczxviA+wETzfE5YCzVuWLyVQBvADfjbFmR8lzGmIM4y3cBmoA/\nTZNcXwUYgMi4AAAE7ElEQVQ+CmTjLFB4Pk1y/Rrwa9EPA8AOnJMd/1cqs0V/Jh/E+ZmcBj5DGrzG\njDE5wLeBtTivsy9E8yxprrQfuRtjfg/4O5wXEcBfAn9orb0eZyXOkmxrEIdPAT3RHLcA/ydNst0O\nYK29FvhDnKJKh1yzP3x/C4xGP5XyXMaYAOCx1t4Y/e/ONMl1I3ANzlYeNwAr0yEXgLX2gdnnC+cX\n9RdxVsylOtttgN9aew1wF+nz2v8MMGStvRr4LySpK9K+3IETwH+I+XgnzggG4DHgpqQncvwA+Hr0\nzx6cEULKs1lr/w34bPTD1TgnlaU8V9Q3gb8BZi8RlA65tgN5xpgnjTHPRM/LSIdcH8I5P+Rh4FHg\nR2mS65zotiKXWWvvIz2yNQD+6FLsQmAyTXJtiT421lqLs23LkudK+3K31v4rzl/SLI+1dnb95ux2\nB0lnrR2y1g4aY4LAQzij5HTJNmWMeRD438A/pUOu6D/lu6y1T8R8OuW5gBGcXzofwpnCSovnCygH\ndgGfiMnlTYNcsb4G/I/on9PhORvCmZI5hjM1+a00yXUI+IgxxhMdPNSQhL/LtC/3OcTOS81ud5AS\nxpiVwLPAd6y13yWNsllrfxXYiPMiz405lKpcvw7cbIx5DmeO9h+AijTI1QD8o7U2Yq1tAHqAyjTI\n1QM8Ya2diI72xvjpAkj1a78YMNbaZ6OfSofX/u/gPGcbcf5F9iDO+xWpzrUXZ679ReDjOFNZ00ud\nKxPL/c3ofCTArThPWNIZYyqBJ4Hft9buTZdsxphfjr4RB86odAY4kOpc1to91tobovO0h4BfAR5L\ndS6cXzp3AxhjVuD8c/7JNMj1EnBLdLS3AsgHnk6DXLP2AE/HfJzy1z4Q5t0NC3uBrDTJdSXwtLX2\nOpzp3JPJyJURq2XO87vA/dG9bOpxpkRS4WtACfB1Y8zs3PuXgG+lONsPgW8bY17AeXH/djRLOjxn\n50uHv8u/Bx6IblcdwSn77lTnstb+yBizB3gNZxD2BZyVPKl+vmYZnJKalQ5/l/cAe40xL+KM2L8G\nHEiDXMeBPzbG/AHOCP03gIKlzqXtB0REXCgTp2VERGQBKncRERdSuYuIuJDKXUTEhVTuIiIulIlL\nIUUSLrrvTQtw2Fp7S6rziFwqjdxFHB8HDgM7jTGbUx1G5FJp5C7i+E3ge0Ajzolf/xnAGPMVnJNO\nBoEXgDustXXRk0/+HGfHRh/wJvBFa+3AHF9bJOk0cpdlzxizBbga+D7OfiS/bIwpM8Z8CGff8itx\ndvELxtztK0R3ArXWbsfZ6fJ/JjO3yIVo5C4Cnwd+bK3tBXqNMU04I/dK4AfW2j4AY8xfAx+I3ucj\nQDHOZmjgnO5+NtnBReajcpdlzRiTj7OJ2Zgxpjn66UKcvVy+h7NX/6zYnfx8wJestY9Fv04B715Q\nRiTlNC0jy91/wtkobIW1ts5aW4dzObQC4CDwc8aY2a12fwNnczGAJ4DfMsZkRy8OcT/wZ0lNLnIB\nKndZ7j4P/KW19tyoPDoN8y2cN1bvB/YbYw7g7Kc+Er3ZHwPNOG+kvoMzwv/d5MUWuTDtCikyj+hl\n5K6x1n4r+vGXgaustb+Y2mQiC9Ocu8j8GoDfN8Z8Fmc6ppV3r08rktY0chcRcSHNuYuIuJDKXUTE\nhVTuIiIupHIXEXEhlbuIiAv9f20Vd5wiO3WqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c703cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Age.values, bins=30, kde=True)\n",
    "plt.xlabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#zero is null\n",
    "Null_label = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "data[Null_label] = data[Null_label].replace(0, np.NaN)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#fill null data\n",
    "Fill_Null = data.median() \n",
    "data = data.fillna(Fill_Null)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qbYxpUY8vdhwMzBMdER5Yts/7q+l4S7Wgx52dzv0cOM//Ofjkq8DnADL3hZPm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1sV6AlsB+wJ3PxHv1AtJyIiIiIiCZfQAfHdgOtzt18ALl9qeynwJdA4d8n80S9UcSIiIiIiIr/L\nzP4G9Frq4e+AebnbC4BVfuWpXwOTgQLgmj/6d1SciIiIiIgkXDZTE/Tfd/d7gXvrP2ZmTwJNc3eb\nAj8v9bS9gRZA29z9f5vZu+7+wW/9OxpzIiIiIiIi/4t3gR6523sDby+1/SegHKh09wqi4mXV3/uF\najkREREREUm40C0nv+FO4EEzeweoIpqVCzPrDXzu7k+bWXdgtJllgHeAl3/vF6o4ERERERGR/5q7\nlwGH/srjN9a73Rfo+5/+ThUnIiIiIiIJl9CWk2VOY05ERERERCQR1HIiIiIiIpJw2Rq1nIiIiIiI\niMRGLSciIiIiIgmnMSciIiIiIiIxUsuJiIiIiEjCqeVEREREREQkRmo5ERERERFJOLWciIiIiIiI\nxEgtJyIiIiIiCaeWExERERERkRip5UREREREJOHS0nKi4kRik8lk6H/XI/iMrykuKqL/mcfQpsVa\nddufe/sDhj3zKoUFBbRv3ZIrTj2CmkyWy257kJnf/0B19SJOPbQHu3XtHHAv/qw8Vtn7cIrWXo9s\nzSJ+fnYENT/N+cVPrdLjSDLlZSx4/SkoKGTV/Y6lcNXmZKrKmffCo7/6nBVBJpOh/6334tO/jP4G\nzj2VNi3XWeJnyisqOeniqxjQ6zTatW7JyJfe4KmX3wSgsqqKKdO+5K1H76JZk8YhduFPy2Qy9B/y\nUO59UEj/s46jTYu167Y/99b7DHv6FQoL8mnfZj2uOO0o8vOjRu7x/gU3DvsnDw68IFT8ZSqTydD/\nzhFMmR69FgPOPp4269Z7Ld4czbCnX6agoIAObdbjitOPrnstVmSZTJaBj76Af/M9xYUFXHn0PrRe\na/W67S+PmcJ9L40iD+jRdROO3q1r3bYJ02dy88jXuK/3MQGSLxuZTIb+t92Hf/FV9B7odQpt1v2V\n48AlVzOg1ym0a9WSkS+9ufg4UF0dHQceuXOFPg4MuOdx/MuZFBcV0u+0I2mzzpp125975yOGP/8G\nBQX5dGi1LpefdFjd3/4P8xZw2EXXc/dlZ9JuqePnymT9rl046LqLuHHXw0NHSYxMSoqTFf8on1Bm\n1tbMnjCzN8zsXTO7w8ya/s7PH2hm68aZMW6vvj+OqupqHrnuInofcyDX3//Pum0VlVXc8tC/eGBA\nHx665gIWlpXzxkef8Mybo1m1aWNGXH0+d11xDlfd/UjAPfjzGlpn8gqLmPvAIOa/9hTNuh/8i59p\ntEU3itZqufj+5juQrapg7gPXM+/Fx1llrxX3QP3qqA+jv4Gbr6L3iUdw/dDhS2yfOHUax553JV99\n+13dYwfuuQsPDurLg4P60ql9Oy454/gV9oQE4NX3x0avwfWX0PvYg7n+vn/UbYveB0/xwMDzeOi6\ni6P3wYcTALj3yRe44vYHqaxaFCr6MvfK6LFUVlXz6A2X0vu4Q7j+vsfqtlVUVjF4xEgeGHgBD19/\nCQvKynjjw/EB0y47r413KqtrGHHB8fQ8YDdueOKVum01mQyDn3qNoT2PZPgFx/PYmx/z08IyAO57\n6T2uHPEcldUr9t/Aq6M+oqqqmkdu7p87DoxYYvvEqdM49vx+Sx0HdubBQVfw4KAr6LRhWy45/bgV\n+zjw4QQqq6t5eGAfeh25P4OGjazbVlFVxa2PPcf9fc/hoQG9WVBWzhtjJgFQvaiGfkMfpUFxUajo\nsdjz/FM55p5rKWzYIHQUCUDFyXJgZiXA08D17r6Lu+8AvA/83pl1T6BZHPlCGfPp53TbvBMAna0d\nk6Z9WbetuKiQh669kJIGxQAsqqmhQXERf9l+S8458q/RD2WzFBYUxJ57WSputQEV0yYDUD1zOsUt\n2iyxvWi9dhSv25bSMW8vfqx5CyqnRR9MNT9+R2HzFfebsjGTnG5bRS1fnTfuwKTPpi2xvaq6mluu\n6EO7Vi1/8dyJU6fx+ZffcFiP7rFkXV7GTP6cbptvAkBn24BJn8+o21ZcVMhD111ESYPoA7n2fQDQ\nap21GHzRGbHnXZ7GTP6MbltGr0WXjTZg4mcz6rYVFxXy8PWXUJI7OampydCgaOU4IRs77Wt26NgO\ngM7tWjL5y2/rthXk5/NU39NoWtKQnxeWk8lmKcod91o1X42bTj0kSOZlacnjQHsmffbFEturqhdF\nx4H1fvl93eLjwO6xZF1exkz5gm5dOgLQuUNbJk37qm5bcWEhIwb0qvs8rMlkaFAUdXS5YfhIDtuj\nG2uttkr8oWM0Z9qX3HXQaaFjJE42U7PcL0mgbl3Lxz7Am+7+fu0D7v6gmZ1uZg8Cj7j7i2a2F3A4\n8A+gCzDMzLoBFwAHEP3/3Onud5lZn9zPLgLecvcLzexKYEOgObAGcDtwMNABOM7dR5vZ2cCRQBZ4\n1N1vieMF+DULyyto0qik7n5+fh6LamooLCggPz+f5qtGtdmI516jrKKS7TtvTF5eHgCl5RWcO+iu\nxYXKCiqvQQnZyvK6+9lsBvLyIZshv0kzmu64Dz/9YwgNO25Z9zPV331Dg/abUuHjKWrZloKmq0Je\nHmSzIXbhT1lYVkaTxo3q7ufn59f9DQBs0Wmj33zu0EdHcsZRv2xpWtEsLCunSeP674P8pd4H0UnH\niGdfjd4HuROYPbffkpnfzQ2SeXlZWFZO03rHhIKlX4vcCdiIZ16hrLyC7XNfbqzoFpZX0qRk8TfC\n0d9AhsKC6PvCwoJ8Xhk7hasffZEdN9mQkgZRUbbHFhsx84efg2RelqL3wO8dB+w3nzv00X9xxtEr\n/nGgtLyCpo0a1t3/5XEg+jx86IU3o+PAZhsx8o3RrNasCd26bMw9T70UKnosxj75Imu0WS90DAlE\nLSfLRztg2q88Ph3YeekH3f05YBxwLNAJ2BvYBugKdDCzTYHDgO1zl/Zmtm/u6eXuvhfwBNDD3fcD\nrgUON7OOwP8B3YAdgQPM7LeP+stZk5KGlFZU1N3PLtUSkslkuP6Bf/Le+E8ZfOFpdYXJt3N/5PjL\n/85+O2/Lvjt1/cXvXZFkK8vJK158UpKXlwfZDAAlG29JfqMmrH7EWTTZ/i+UbLI1JZttS9m4UWQr\nK1jjuD40tM5Uf/vVClmYADRp1IjSst/+G/gt8xeWMv3rb9mmyybLM14smjQqobT8D94H9z/Oe+Mm\nM/ii0+veByujpV+LzK+9Fvc+xqhxkxl88ZkrzWvRpKQBZZVVdfej/V7y47j75hvxyjU9qa7J8Mzo\nT+KOuFxF/+/1v6T5L44D38xim84rfpHauKQhpeWVdfd/7TgwaNhIRk2Yws19TiIvL4+Rr4/mvQnO\n8VcOZsqMmVx823Dm/Dw/RHwJJC0tJypOlo+ZwPq/8viGwFv17v/aJ60BH7h7jbtXuXsfYCNgtLtX\nu3sWeJuoiAEYk7v+GZicu/0T0BDYBGgDvJq7rAG0/1936s/afOMNefvjiUA0sLd96yW77lx550NU\nVVVz60Wn1zVnz/15PidfOZjexx7Ewd13iD3zslb1zRc03DA6wS5q2Zbq72fVbSv98HXm3nsNPwy/\niYWj/k35xA8pnzCaonXbUDl9Cj88+HcqPh1Dzc8r7rfnm3c03v5wLADjP51K+/Vb/0fP++iTT9l2\n8xW/MIHa90F0sjnep9G+zVLvgzuGR++DS86s6961stpi4w1566PotRg3ZRodlnot+t4+jMrqam67\n9Ky67l0rgy7tWvH2xOj7q/FfzKT9uosHQi8sr+SEG4dTVb2I/Pw8SoqLVpqirNbmnTrw9gfjABj/\n6We0X7/Vf/S8jz75lG1Xgi8oADa3drw1NuquO37qdNq3brHE9iuHPkpldTW3nn9y3efhsH7n8mC/\nnjxwZU82Wr8l15x1DGuuulL3BpeUUreu5eNfwKVm1tXdPwAws5OAuUAZUHsU2qLeczJExeIU4HQz\nywcKgOeB84A+ZlYI1AA7AcOAzkTdtX6LA5OAvd09a2a9gAnLZhf/e9236cKocZ9y5EXXkc1mGXj2\n8Tz71geUVVSwyQbr88Sr77LlxhtywhU3AXDMvrvxwcSpzCstY8jjzzPk8ecBuOvys2mYO1ivaCqm\njKNB241oftx5kJfHz88Mo6TT1uQVN6Bs7Du/+pyaH7+n2UEn0bTb3mQqyvn52eG/+nMrgu47bM2o\nMRM48tzLyZJlYO/Tefa1dyirqPjdsSTTv5lFq3XW+s3tK5Lu227OqHGTOfKCa6LX4JwTePbN96P3\nwYbr88Qr77Blx/accPkNAByzb3e6b7fFH/zWFVP37bZg1LjJHHH+QLJZuLrniTz7xmjKKirotGFb\nnnj5bbbs2J7jLx0EwDH7d2eP7bb8g9+afLt3MUZP+YJjBj1ANgsDjt2X5z6YSHllFYfsuAX7bN2J\n428cTlFBPu1brsW+26wcJ+S1um+/NaPGfMKRva4gm4WBfU7l2dffpay84nfHkkz/5ltatVhJjgNd\nN+O9CVM46rIbyWazXHXGUTz7zkeUVVSySbvWPPn6aLbcaANO7H8rAEf32IXuK/RMlbIsZGuS0bKx\nvOVlV9DuIUlnZhsANxG1VhQSFQXnE7We3Ad8D0wFGrn78WZ2FbAXsCdwKrA/UbFyp7s/YGa9ibpo\n5QPvAL2BvsBsdx9iZqcB67j7lWZ2ALCXu59mZucTjV9pAHwAnO3uv/nXXTP5jVT/QXz35GN//EMr\nubWPPjV0hLAqFoROEFzeSjBd759R/c2v9cpNl8K2K1dB9N/Kzl9xW6iXlbO6nBI6QnBDsjMS1Wy5\nRo+By/0c7YfnLw2+zypOZAkqTlScqDhRcaLiRMWJihMVJypOklecrL5X/+V+jvbji1cE3+d0fwKJ\niIiIiEhiaMyJiIiIiEjCJWU2reVNLSciIiIiIpIIajkREREREUk4tZyIiIiIiIjESC0nIiIiIiIJ\nl81kQkeIhVpOREREREQkEdRyIiIiIiKScBpzIiIiIiIiEiO1nIiIiIiIJJxaTkRERERERGKklhMR\nERERkYTLpKTlRMWJiIiIiEjCZWvSUZyoW5eIiIiIiCSCWk5ERERERBJOA+JFRERERERipJYTERER\nEZGEU8uJiIiIiIhIjNRyIiIiIiKScGo5ERERERERiZFaTkREREREEk4tJyIiIiIiIjHKy2azoTOI\niIiIiIio5URERERERJJBxYmIiIiIiCSCihMREREREUkEFSciIiIiIpIIKk5ERERERCQRVJyIiIiI\niEgiqDgREREREZFE0ArxIiIiIglhZu2B9sAEYKa7a0E6SRUVJ5IYZtYMyAAHAs+6+0+BI8XKzHYH\nNgBGA1PdvSJwpFiZWQFwPNAGeA2Y6O5zg4aKmZnlA3nA9sD77l4VOJKIxMjMziL6DFwdeBDYEDgr\naKgAVKClm4oTSQQzexR4luikLB84iOgAnQpmdjWwHrAxUAlcDBwRNFT87gJmAXsAHwLDgB5BE8XI\nzG4GPiUqzrYAvgOOCxoqZmbWFNgbaFj7mLsPC5coHmb2HrD0yVcekHX37QNECsbMWhEd++r/DfQP\nlyh2hwM7Aa+6+81m9mHoQHFTgSYacyJJsa67jwA2dvfTgKahA8Wsm7sfCyx09weBtqEDBbCBu18B\nlLv7M8AqoQPFbGt3vwvYzt33IipW0+ZfwP5ERfrGwEZh48TmcKIT8vqX2sfS5h9AM6LivPaSJvlE\nhWptsVoZMEsohxN9SfWzu98MbBM4j8RMLSeSFMVmdhAw2cyak77ipNDMGgLZXPemmtCBAijM/d/X\nfoOeCZwnbgVmtiUww8yKSd97ACDf3Y8OHSKANr+z7cvYUiTDAne/LHSIgB4B3gLamNnzwFOB84Sg\nAi3lVJxIUlwP/B/QBzgHGBA2TuxuAj4G1gTez91Pm0uBd4EWRONueoaNE7thwB3AiUTvh7vCxgli\ngpltA4wjd2KSknE3bwDTiLozQtSlC6LX4K0QgQKaaGaHA2NZ/DcwNWyk+Lj7rWb2CrAJMMXdPwmd\nKYCHUYGWannZrMYYSTKYWQeivqWpHABnZqsR7f8X7v5D6DxxM7Oj3P0hM1sTmO03+aIAABdwSURB\nVJu2///6zKyVu38dOkfczGw8UZeeWll3bxcqT1zMbAvgSKKxRq8BI9x9RtBQgZjZ60s9lHX33YKE\nCcDM7lvqoWrga+D2NE0SY2Ybk+4CLdVUnEgiLDUA7gGgvbunZgCcmXUnasksAG4BLnf3h8OmipeZ\nvenuO4fOEYqZnQ/8DKwKnAC86O69w6aSOJlZHrAbUaGyDvB0bhxSqpjZGkQzF36Rwhn7HiFqRXsb\n2BbYmqgVqbO77x8yW1zMrCvRuJP6kyKcES6RxE0D4iUp6g+AG0z6BsANBD4DzgZ2AE4LGyeIBmY2\n1sweNbOHzSxVxRlwMNHMNHu7e0dg88B5YmNmt+Wu3zOzUfUvobPFKddaOAp4hejz+aSwieJnZocS\nvQaXAKPNLG1jkNZ098vc/d/u3g8odvfLib60SIsHiXpQ/LveRVJEY04kKdI+AK6MaFaaRe4+28zS\n2KR5YegAgdUQfVteOztRScAscasdY3Z40BSBmFkR0RTKRwJGNGvZue7uQYOF0RvY0t0X5ibGeA0Y\nEThTnJqZ2UbuPiXXtalJriWpSehgMfrM3R8IHULCUXEiSZH2AXDzgReBoWZ2JvB94Dwh/N6MRWnw\nRu5ytJndBDwXNE28eprZb227JM4ggXxPtMbPI8D9ucfamFkbd38pXKwgMu6+EMDdF5hZqhajJVrP\n4yEzawGUE3Vz/j+i1vW0eCK39tnk2gdSttZN6qk4kURw99vM7FWiAXDu7hNCZ4rZYUTrfEw2s07A\nPaEDBbBx7joP6AL8SDSDVSq4+6XApWa2OnBhSmapqpXGFoL6/kXUatwud6ltNSsH0lacfGFmfyf6\nsmonovEXqeHuH5jZ6URFyp7A2u6ettkrzwSeIBqDJymk4kSCMrOT3P0eM7uGxV26Njezw909Dd+Y\n1loT6GdmHYGpQC9gRtBEMXP3i2tv5wYGPxswTuzMbCeiqYQLgH+Y2Zfufm/gWLHILTxa+xqk0c1E\nXdu+Ax4DHs093itYonBOAE4lGoM4GbgobJx45NY2OoLoxLySaNa6tu5eHjRYGD+4+3WhQ0g4Kk4k\ntNrpUqcETRHe3cCdRN8W7gLcC+weMlDcch/OtVoAbUNlCeQqom+KnwCuJlrzJRXFST2n567zgE5E\nBXoa1vm4A7gCWAMYSTQZwhyirp6paD00s63c/SOi2co+y10AdiUdrUcziLr1HeXun5nZCyktTADm\nmtldwBgWr3UzNGwkiZOKEwnK3Wtn4XCgq7vfYmYPAX8PGCuEhu7+dO72U2aWxilkneiDKI+oO8ug\nsHFil3H3H80s6+4VZrYgdKC4ufsRtbdzxerjAePEqcrdXwEws57u/lnu9sKwsWK1O/ARUetBfVnS\nUZzcDBwFrG9m97B4Ic40+jx3vU7QFBKMihNJiltZPFPP5USDANPUxaPQzDZ190/MbFMWd3FLDXev\naykxs3x3z4TME8Dnue6Na5jZRcCXoQMFVkg0/iIN6v+t1x8Anprp/ut143nX3evG3JnZOYEixcrd\nrweuN7OdiaaQ3trMrgOGu/vEsOni5e79zGwfotZTd/d/hc4k8VJxIklR7e7TANz9CzNL24npOcB9\nuRlaZgGnBM4TOzM7img63QZEH9KD3P2GwLHidBrRSck7wELg5LBx4mdm37K49awQGBw2UWw65db1\nyVvqdsewseJjZkcA+wO7mlntivAFRJOk3BIsWMzc/U3gTTNbFTgGGE6K1jwCyH1J057oWHicme3o\n7ucFjiUxUnEiSfGlmV0NvAd0BWYGzhO3ycAp7j7WzA4AJoUOFEBPorUeHgVaE3XlSFNxUkPUx7p2\n+sxtScd4izru3iJ0hkAOq3d7yG/cXtm9CHxLNO5mCFFxliFls3XVcvefiXoU3Bo6SwA7ufsOAGY2\nGBgdOI/ETMWJJMUJRN8c9yA6ObsqbJzYPUS0rsVYoAPRycqRQRPFr3bw5wJ3rzSztB2fngSaE00S\nkUfUgpCq4sTMuhN9LuUTnZRd7u4Ph021/OW+LU81d/8JeMPMvgG2dvdHzOxa0lWgSaSoXtfe2mOh\npEjaPvwluSqBUUTfHANsQ7pOzFq6+/0Q9T02s9dDBwrgC6JvyHqZWV8gbWvdrO3u24cOEdhAoqL8\ndmAHogHxK31xIkt4EOiTu/08KZy5UHgMeNfMRhOdCzwWOI/ELDWD7STxniCaoes0oulETwsbJ3ZZ\nM+sAYGYbEPW1ThV3PwHY3N2fBYa4++l/9JyVzBQzWzd0iMDKiNb6WOTus9E3pqnk7qNz12+h85TU\ncfe/E425exc41d1vChxJYqaWE0mKdVL+rXEv4DEzW4dovE3airO6Lj1mlg/camap6NJTz47AV2Y2\nJ3c/6+5pK1bmE409GGpmZwLfB84j8fvZzE5h8fjD1E2pnXZmdjLQwd3PN7OXzGy4uw8PnUvio+JE\nkmKKma3r7rNCBwnB3d8nZTOy/IpUd+lx9/b175vZ6qGyBHQYsIG7TzazTYgWJ5V0OQ64DDiQaPzh\niWHjSACnExWmAPsQdfFWcZIiKk4kKbqR4m+NzWw6S3Zhme/uXULlCWSJLj1mlqouPWZ2q7ufnbu9\nJ3Ab0eQIabIWsK+ZHVLvsf6hwkj83H1ububGhrmHGoXMI0HUuPsiAHevTttngag4kYRw97SdhC1t\no9x1HrAlcGjALKGkvUvP/NzsRE2I1nbYO3CeEP4BvEI0Y5mkkJndQfS3/y2LZ2pKc5ffNPqXmb0N\nfABsATwdOI/ETMWJJIKZbUs0nXAR0QfSuu7+l7Cp4uPulfXuvptbhCptlu7Sc88fPWFl4u6Xmtkg\nYEN33yV0nkAWuPtloUNIUF2JjgNpW4hXFrsWeBYwYBjwZdg4EjcVJ5IUdwLXA4cAnwDFYePEK1eM\n1DZdr0u0+FjaNAcuMbO1iL5Bbwy8HzbS8ldvVXSICvO1zWwWQJq6NuZMNLPDidb7yQK4+9SwkSRm\nnxN16SoLHUTilZsQphlRQXIM0XEgn2hB3q6/81RZyag4kaSYm1t0a093v9LM0rYo2ZR6t8cTdW9K\nm6FE00lfTjQA8kGiVdJXavVXRTezxu5emuLJIboAnZd6bLcQQSSY1sCXZvZ57n425TM5psm2QE+i\nFpOhuccywL+DJZIgVJxIUmTMrBPQyMwMSMVMRbmBzxD1r65vG6Jvi9KkxN1fM7PL3N3NrCJ0oDjl\nFp5sAFwCDDazj9z9usCxYmFm7xG1lOQttUkDYdPniNABJAx3fwp4ysx6uPvzofNIOCpOJCl6A52A\nW4imj70vbJzYHMHik7KWwDdAG6I+tmkrTirM7C9AQW4MUqqKE2B/d98SwN0PNbN3gVQUJ8DhoQNI\nYhz3K49pxrZ0OXSpGftwd00pnSIqTiQR3H0SMCl3d8uQWWI2CLjN3XczsylAU2A94I6wsYI4BbiB\naOzJeURz3adJxsyK3b3KzIpI0crY7q4Br1Lru9x1HtFMTal5H0idR3PXtX8DaRt7l3oqTiQRzOwS\n4ALqDYJMyWDg64j2G+Bbd9/VzDYkmqnqiXCxgujl7mn+Bn0I0YDwT4imlr4+cB6R2Ln7XfXvm9kL\nobJIGO5ef4zJi2aWtl4EqafiRJLi/4imD07bDC2N3P2j3O15AO7+uZml8b3Z0cxWdfefQwcJwd3v\nNbOngXbANHefGzqTSNzMrP6aVy2IurlKitQbiwlRq8naobJIGGk8AZJkmg6Uhw4RQEntDXc/oN7j\n1QGyhNYRmGtmc4nG4WTT0HqWmwDgKjN7hHoDwM0Mdz8yYDSREOq3nJQDfUIFkWCOBHYimrWxDNB4\nk5RRcSJJUQx8kuvSAtGJaRpOzGaaWVd3/6D2ATPrCswOmCkId0/rN6TP5K6HBE0hEpCZrefu37j7\nrqGzSBhm1gR4hGjc4WiiL6zmAJ+FzCXxU3EiSZGWWYmWdgHwtJm9SrT4WDtgd2C/oKliZGY7AzcC\nC4CT3P3zP3jKSsXdx+dufkm0CGmjepvTtt6PpNcwcmvamNnF7n5N4DwSv2uBf7j7sNoHzOxvRBPH\nnBoslcROs2BIUuy81GV7Mzs6N2vRSsvdpxOtfDuKaEX0j4Dt3f2roMHiNRA4CrgUSPMJySNEfwPf\n1buIpEX9NW72CJZCQupcvzCBaCwesFmgPBKIWk4kKToT9S9+m2iV2FZECxP+BTgmYK7lzt3LgcdD\n5wioyt2nAJhZv9BhAipz9zTvv6SbFtyU3xpruSjWFBKcihNJilXd/eDc7bvM7CV3P8bM3gmaSuKW\nutbcerMTfWdmRwBjyJ2oufvUYMFE4rWGme1BdAxYvf6MTe6uqWTT4Ucz26reDJaY2VbAjwEzSQAq\nTiQpVjWz5u4+18zWAFbJdelq9EdPlBVeSzM7hahbR+1tANx9aLhYsak/O9Ep9W5nyfXBF0mBMUSz\nNAGMBY7I3c4CKk7S4TyiMZhvANOAtkB3UjQGUyJ52axaUiU8M9sXGAzMB5oAZwNdgAXufnvIbLJ8\nmVnf39qWhm5OZtbD3Z8PnUMkScxsdeAnd9dJSoqYWUNgH6LJYWYC/3L30rCpJG4qTiQxzCyfaNGt\nWfpAkrQws9fcXS0kIoCZ7QTcARQA/wC+zA2KFpGUULcuSYTcdLK3k/tAMjN9IKWEmX1L1HWjAVE3\nvq+BlsAcd18/YLS45Oe6MOYtvcHdqwLkEQnpKqIF+J4ArgbeBfRZIJIiqRt8Kok1gOgDaTbRB9IZ\nYeNIXNy9RW4l+BeADu7eAWgPvB82WWy2ATx3mZK71N4WSZuMu/9ItBBvBdH6RyKSImo5kaTIuPuP\nZpZ19woz0wdS+rRz968B3H2WmbUOHSgmo7Uqtkidz83sGqC5mV1EtDipiKSIihNJitoPpDX0gZRa\nk81sOPABsD3wceA8IhK/04CTiNa8WgicHDaOiMRN3bokKc4gKkjeAUrRB1IanQI8RbRK+iPuflbg\nPHE5O3QAkQTJEi269wMwEWgWNo6IxE0tJ5IUz7r7nn/8Y7ISawJsC3QC1jazd3J9z1dq7j4RILcA\nXW+iiQFqt2kWL0mbu4BZwB7Ah8AwoEfQRCISKxUnkhQ/mdlfiQYCZ0CrY6fQfcCbwEPAzsADwP4h\nA8XsJuBcotnKRNJqA3c/ycx2dPdnct18RSRFVJxIcGbWjGjBpXPrPazVsdNnDXe/NXd7nJkdEjRN\n/L5y91dChxAJrNDMmgNZM2tK7ssqEUkPFScSlJmdBfQBaoDL3f3FwJEknBIzW8fdZ5vZ2kRr3qTJ\n92Y2BBhLVJzj7kPDRhKJ3aVEa5u0AEYDPcPGEZG4qTiR0I4EjGjQ43BAxUl6XQ6MMrP5QFPSNynC\n9Nz1OrnrbKggIgG1cnczszWBue6u94FIyqg4kdAqcqtgzzWz4tBhJBx3fxloZ2ZrAT+4e03oTHEw\ns/Xc/RvgkdBZRBLgFOAhd58TOoiIhKHiRJIkL3QACcfMdgXuBeYBq5nZybmCZWXXO3e5i8WtJXlo\n3JWkUwMzG0s0OUqWaKX4IwNnEpEYqTiR0DqZ2cNEJ2O1twHQB1LqDAC65VaHbwk8Caz0xYm7987d\nPMDd59U+bmbbB4okEtKFoQOISFgqTiS0w+rdHhIshSRBjbvPAnD3mWZWETpQzJ40s32IFqAbAPwF\n2CJsJJHY7bzU/WozawU85u7VIQKJSLxUnEhQ7v5m6AySGPPN7GzgLWAnYKVfgHEpNwNPAasB/wa2\nCRtHJIjOQDnwNtGirK2Ab4mK9WMC5hKRmOSHDiAiknM00BoYSHRCcmLYOPEwsw5m1oGoj/2bwHxg\nBNA2aDCRMFZ196Pd/S53PwHIuPsx6P0gkhpqORGRoHIn5rXuZvFg8DWBn4KEilftQPjaCSFWI5q5\nax4aEC/ps6qZNXf3uWa2BrCKmRUBjUIHE5F4qDgRkdDqz1IFUJK7LicdJ+d9gPuArsC+RGOvfgb6\nhQwlEkhf4P3cekdNgLOJ3iP3Bk0lIrFRcSIiofUiGgD+HfBY7pLNPZ4Gg4Bj3b3KzK4C9gI+B14A\nng6aTCRm7v6smT1P1HL6fW4RRi3OK5IiGnMiIqHdAQwGXgJGEg0E3wA4I2SoGBW4+wQzWxdo7O5j\n3H0+WiFeUsTMbstdvwe8Q3QseNfMRgUNJiKxU8uJiIRW5e6vAJjZue7+We72wrCxYlM7PepeQO3r\nUETUpUUkLQbkrg8PmkJEglNxIiKhZerdrr+2SVpadl8xs3eJZijb38w2AG4j6t4mkhanmtlvbesf\nZxARCUvFiYiE1snMHiaarar+7Y5hY8XD3a8zs6eBee4+K1ecDHX3kaGzicTou9z1AcB04F1ga6Lp\nxUUkRfKyWXVrFpFwzGzpFaHraJFOkXQxs5fcfc9691929z1CZhKReKnlRESCUgEiIvWsbmYbuPs0\ni/p5rRI6kIjES8WJiIiIJMW5wEgzWwuYCZwWOI+IxEzdukRERCQxzGwVYH1gmrunZdY+EclRcSIi\nIiKJYGYHA5cR9ex4HMi6+1VhU4lInNIyVaeIiIgkX29gW2AucBVwYNg4IhI3FSciIiKSFDXuXknU\nYpIFSkMHEpF4qTgRERGRpHgnt9bRemY2BPgwdCARiZfGnIiIiEhimNlewKbAFHd/JnQeEYmXihMR\nEREJyszWAc4DFgKD3F3duURSSt26REREJLQHgc+BKuD6wFlEJCAtwigiIiKhFbn7EAAzeyV0GBEJ\nRy0nIiIiElr9PuY6NxFJMbWciIiISGiNzaw9UWHSKHc7D8DdpwZNJiKxUnEiIiIioZUDQ3/ldhbY\nLUgiEQlCs3WJiIhI4phZvrtnQucQkXipOBEREZFEMLOjgBqgAdGsXYPc/YawqUQkThp0JiIiIknR\nE3gZOBpoDewXNo6IxE3FiYiIiCRFee56gbtXorGxIqmj4kRERESS4gtgNHCfmfUFJgTOIyIx05gT\nERERSQwza+LuC81sHXefHTqPiMRLxYmIiIgkgpl1AoYAqwEjgInu/mzYVCISJ3XrEhERkaS4BTgB\nmAPcC1wZNI2IxE7FiYiIiCSGu38OZN19DrAgdB4RiZeKExEREUmKH83sVKCxmR0O/Bw6kIjES8WJ\niIiIJMXfgLbAXGAr4MSwcUQkbpo/XERERJLiHHe/qPaOmV0DXBwwj4jETLN1iYiISFBm9jfgJGBj\nYHLu4Xyg2N23CBZMRGKnlhMREREJbQTwKnAJMDD3WAb4PlgiEQlCLSciIiKSCGZWCBwHtAFeI1rn\nZG7YVCISJw2IFxERkaQYQlSY7AE0BYaFjSMicVNxIiIiIkmxgbtfAZS7+zPAKqEDiUi8VJyIiIhI\nUhSaWXMAM2tKNO5ERFJEA+JFREQkKS4D3gVaAKOBc8PGEZG4aUC8iIiIJIaZ5QPNgTnurpMUkZRR\nty4RERFJBDPrAXwOvAi4me0SNpGIxE3FiYiIiCRFX2Cb3MKLOwHXBs4jIjFTcSIiIiJJscDd5wC4\n+2ygNHAeEYmZBsSLiIhIUGZ2de5moZk9C7wDdAUqw6USkRBUnIiIiEhovtQ1wL9CBBGRsDRbl4iI\niCSCmRUCWwNFQB6wrrs/EjaViMRJLSciIiKSFCOJCpOWQAEwC1BxIpIiGhAvIiIiSdHc3fcC3ge2\nBBoGziMiMVNxIiIiIklRlrtu7O7lgPqei6SMxpyIiIhIIpjZmcAaQBVwALDQ3buHTSUicVJxIiIi\nIoljZpsCn7l7RegsIhIfDYgXERGRoMzsMne/yswe/pXNR8YeSESCUXEiIiIioT1jZpsBrYDmwHBg\nDjA1aCoRiZ0GxIuIiEhoHYD7gGHAhcAC4GxgtZChRCR+ajkRERGR0HoCO7t7ae0DZvYg0SrxTwVL\nJSKxU8uJiIiIhLaofmEC4O7zgZpAeUQkEBUnIiIiElrmNx7XeYpIyqhbl4iIiITW6Vdm6soDOoYI\nIyLhqDgRERGR0A77jceHxJpCRILTIowiIiIiIpII6sspIiIiIiKJoOJEREREREQSQcWJiIiIiIgk\ngooTERERERFJhP8HLOZAASxIaQUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c350cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check for correlationship\n",
    "import matplotlib.pyplot as plt\n",
    "cols=data.columns \n",
    "data_corr = data.corr()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "plt.savefig('correlationship.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = data['Outcome']\n",
    "X_train = data.drop(\"Outcome\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 8)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### default Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [ 0.48797856  0.53011593  0.4562292   0.422546    0.48392885]\n",
      "cv logloss is: 0.476159709444\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lihuixiang/anaconda3/envs/py27/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print 'logloss of each fold is: ',-loss\n",
    "print'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化的 Logistic Regression及参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                  'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00106659,  0.00104594,  0.00161595,  0.00162921,  0.00100379,\n",
       "         0.00103197,  0.00098763,  0.00095606,  0.00114956,  0.00107322,\n",
       "         0.00118437,  0.00127115,  0.00186963,  0.00121942]),\n",
       " 'mean_score_time': array([ 0.00101905,  0.00096264,  0.00169497,  0.00135565,  0.00071402,\n",
       "         0.0007174 ,  0.00072823,  0.00066972,  0.0007926 ,  0.00082974,\n",
       "         0.00087643,  0.00077744,  0.0014298 ,  0.00099835]),\n",
       " 'mean_test_score': array([-0.69314718, -0.62766704, -0.63597526, -0.51497115, -0.47856668,\n",
       "        -0.47681232, -0.47602561, -0.4762194 , -0.47646596, -0.47649922,\n",
       "        -0.4765282 , -0.47653284, -0.47653175, -0.47653626]),\n",
       " 'mean_train_score': array([-0.69314718, -0.62652347, -0.63462991, -0.50971499, -0.46988112,\n",
       "        -0.46565315, -0.46285714, -0.46277622, -0.46273091, -0.46273001,\n",
       "        -0.46272953, -0.46272951, -0.46272951, -0.46272951]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.01 0.01 0.1 0.1 1 1 10 10 100 100 1000 1000],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'param_penalty': masked_array(data = ['l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2'],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'params': ({'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}),\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  9,  1,  2,  3,  4,  5,  7,  6,  8], dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.62523328, -0.6359066 , -0.51207677, -0.489221  ,\n",
       "        -0.48350008, -0.48815661, -0.48797856, -0.48903396, -0.48902949,\n",
       "        -0.48914066, -0.4891444 , -0.48914488, -0.489156  ]),\n",
       " 'split0_train_score': array([-0.69314718, -0.62745539, -0.63279271, -0.50890565, -0.46651589,\n",
       "        -0.46237618, -0.45927697, -0.45918946, -0.45913773, -0.45913682,\n",
       "        -0.45913626, -0.45913625, -0.45913624, -0.45913624]),\n",
       " 'split1_test_score': array([-0.69314718, -0.6338686 , -0.64460736, -0.54322757, -0.524889  ,\n",
       "        -0.52514943, -0.52916522, -0.53011593, -0.53103323, -0.53113632,\n",
       "        -0.53123432, -0.53124692, -0.53125267, -0.53125807]),\n",
       " 'split1_train_score': array([-0.69314718, -0.6232271 , -0.62102867, -0.50035915, -0.4584199 ,\n",
       "        -0.45417418, -0.45114257, -0.45108423, -0.4510343 , -0.4510337 ,\n",
       "        -0.45103317, -0.45103316, -0.45103315, -0.45103315]),\n",
       " 'split2_test_score': array([-0.69314718, -0.62566121, -0.63328109, -0.50673804, -0.45825208,\n",
       "        -0.46048749, -0.45561899, -0.4562292 , -0.45589273, -0.45595951,\n",
       "        -0.45592523, -0.45593526, -0.45592785, -0.45593286]),\n",
       " 'split2_train_score': array([-0.69314718, -0.62777054, -0.64122227, -0.5128013 , -0.47460056,\n",
       "        -0.46996298, -0.46735787, -0.46727381, -0.46723249, -0.46723158,\n",
       "        -0.46723114, -0.46723113, -0.46723113, -0.46723113]),\n",
       " 'split3_test_score': array([-0.69314718, -0.62087342, -0.63024185, -0.48731256, -0.4333573 ,\n",
       "        -0.42929827, -0.42237386, -0.422546  , -0.42195563, -0.42197953,\n",
       "        -0.42192329, -0.42192491, -0.42192034, -0.42191947]),\n",
       " 'split3_train_score': array([-0.69314718, -0.6300762 , -0.64391789, -0.5193933 , -0.48292535,\n",
       "        -0.47815891, -0.47566754, -0.47558312, -0.47554385, -0.47554298,\n",
       "        -0.47554256, -0.47554255, -0.47554255, -0.47554255]),\n",
       " 'split4_test_score': array([-0.69314718, -0.63268716, -0.63580105, -0.52538884, -0.48687441,\n",
       "        -0.4853734 , -0.48452015, -0.48392885, -0.48410994, -0.48408647,\n",
       "        -0.48411215, -0.48410727, -0.48410756, -0.48410941]),\n",
       " 'split4_train_score': array([-0.69314718, -0.62408812, -0.63418802, -0.50711554, -0.46694392,\n",
       "        -0.46359351, -0.46084077, -0.4607505 , -0.46070616, -0.46070497,\n",
       "        -0.4607045 , -0.46070449, -0.46070448, -0.46070448]),\n",
       " 'std_fit_time': array([  2.79993590e-04,   5.42413533e-05,   1.27296268e-04,\n",
       "          4.69202448e-04,   1.47683589e-04,   1.24222221e-04,\n",
       "          4.28895343e-05,   2.57858263e-05,   1.72234189e-04,\n",
       "          1.24527519e-04,   1.99218867e-04,   1.57657148e-04,\n",
       "          3.38847289e-04,   1.54638562e-04]),\n",
       " 'std_score_time': array([  2.09385348e-04,   1.22433417e-04,   1.77319346e-04,\n",
       "          5.64067643e-04,   8.54116915e-05,   8.76109613e-05,\n",
       "          6.17872408e-05,   2.97872505e-05,   1.18864381e-04,\n",
       "          1.58420877e-04,   1.64252787e-04,   7.23728855e-05,\n",
       "          6.16757354e-04,   3.64946272e-04]),\n",
       " 'std_test_score': array([  1.11022302e-16,   4.89097299e-03,   4.79089060e-03,\n",
       "          1.86921266e-02,   3.09307799e-02,   3.15479636e-02,\n",
       "          3.55959881e-02,   3.57224624e-02,   3.62933535e-02,\n",
       "          3.63084245e-02,   3.63674855e-02,   3.63697206e-02,\n",
       "          3.63737095e-02,   3.63758791e-02]),\n",
       " 'std_train_score': array([ 0.        ,  0.00252359,  0.0079786 ,  0.00629506,  0.0082914 ,\n",
       "         0.00802142,  0.00822773,  0.0082209 ,  0.00822505,  0.00822498,\n",
       "         0.00822503,  0.00822503,  0.00822503,  0.00822503])}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the complete results (list of named tuples)\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47602561147\n",
      "{'penalty': 'l1', 'C': 1}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SfzxnONGRjmbnhnpZAtVRygFSllDVUcoSjs1Hoo2iHdH8cdhvGZhyPLb4PRTGfsl9b82l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\n6exypdRFQKzW+oVDXPMS8KpSag6++bi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c1947d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = -grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = -grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# the best model is L1, with c=1. the result is 0.47602561147"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_part, X_val, y_train_part, y_val = train_test_split(X_train, y_train, train_size = 0.8,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "SVC1 = LinearSVC().fit(X_train_part, y_train_part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.91      0.87       107\n",
      "          1       0.74      0.60      0.66        47\n",
      "\n",
      "avg / total       0.81      0.81      0.81       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[97 10]\n",
      " [19 28]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "y_predict = SVC1.predict(X_val)\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, metrics.classification_report(y_val, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_val, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.798701298701\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.753246753247\n",
      "accuracy: 0.753246753247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lihuixiang/anaconda3/envs/py27/lib/python2.7/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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gU3HXkLv/ClgNrCAYU/h8YaxBRKRkra1aB6m/Qus+cvcccFmfh9cV7e8kuBS1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\nRKSXQkFERHr9f9dvNAVIHcP4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c0ff1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C_s = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_val, y_val)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    accuracy = SVC3.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.779220779221\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.896103896104\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.792207792208\n",
      "accuracy: 0.798701298701\n",
      "accuracy: 0.831168831169\n",
      "accuracy: 0.961038961039\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 0.75974025974\n",
      "accuracy: 0.798701298701\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.863636363636\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 0.798701298701\n",
      "accuracy: 0.798701298701\n",
      "accuracy: 0.818181818182\n",
      "accuracy: 0.88961038961\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = [0.001, 0.01, 0.1, 1, 10, 100, 1000] \n",
    "gamma_s = np.logspace(-5, 5, 10)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_val, y_val)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.746753246753\n",
      "accuracy: 0.798701298701\n",
      "accuracy: 0.75974025974\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.857142857143\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.883116883117\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.811688311688\n",
      "accuracy: 0.928571428571\n",
      "accuracy: 0.805194805195\n",
      "accuracy: 0.863636363636\n",
      "accuracy: 0.987012987013\n"
     ]
    }
   ],
   "source": [
    "C_s = [0.001, 0.01, 0.1, 1, 10, 100, 1000] \n",
    "gamma_s = np.logspace(-3, -1, 3)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_val, y_val)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ghFJqPd57Bg8opSYC0Vrr2Uqp14C1Sqlq4BDwZk27N2uGvxrAgxe7dCREc6nO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\nUoiIiFqSQkRE1JIUIiKilqQQERG1vwGIo3bL6HxIdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2c5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
